The topic of
multi-modal biometrics has attracted great interest in recent years. This talk
will categorize different approaches to multi-modal biometrics based on the
biometric sources, the type(s) of sensing used, and the depth of collaborative
interaction in the processing. By ?biometric source ? we mean the
property of the person that is used for identification, such as fingerprint,
voice, face appearance or iris texture. By type of sensing we mean different
sensor modalities, such as 2D, 3D, or infra-red. By collaboration we mean the
degree to which the processing of one biometric is influenced by the results of
processing other biometrics. One common category of multi-modal biometrics
might be called orthogonal. In this category, the biometric sources are
different, such as face plus fingerprint used as a multi-modal biometric or a
multi-biometric. In this category, there appears to be little or no opportunity
for interaction between the processing of the individual biometrics. Another
common category of multi-modal biometrics might be called independent. This
type of processing is common with different modalities of sensing the face. For
example, the 2D image of the face and the 3D shape of the face might be
processed independently as biometrics, and then two results combined at a score
or rank level. A less common category of multi-modal biometrics might be called
collaborative. In this category, the processing of each individual biometric
may be influenced by the other biometrics. For example, if specular highlights
are found in the 2D face image, this might inform the processing of the 3D
shape of the face, since specular highlights in the 2D often result in
artifacts in the 3D shape. It is argued that the area of collaborative
processing among multi-modal biometrics, although relatively less explored,
holds the potential for important gains in accuracy.